Tag: Venture Capital

A Venture Partner Apprenticeship

I wrote elsewhere that my successes taught me more although my failures caused me to learn more. Coming off a year of a short-lived attempt at building a data-curated commerce engine, the need to learn more has never been more clear. I am not the kind of person who locks himself up in a room and emerges after a period of learning and/or contemplation. My best thinking is done by engaging with people, not just content.

For me, the best content are people and their insights.

My search for the right place to learn was quickly ended by the remarkably insightful folks at True Ventures. Since their founding in 2006, they have been focused at creating a unique and strong trust-fabric between founders and the firm. In fact, the word ‘firm’ is a bit of a misnomer, True is an intensely collaborative environment where egos haven’t formed silos of operation and partner founder relationships are not transactions.I am honored and excited to have a chance to learn in True’s supportive environment.

In the next one year I will be focusing on engaging with and learning from people. The fastest learners and consequently the best teachers (for me) are founders. The good ones have this uncanny ability to freeze everything and advance the world along a singular axis of their vision of a product, a technology, or a need that has not been found yet by others.

At True Founder Camp I had an opportunity to meet and talk to many True founders. I’d love to engage further and be of service wherever I can. My time is yours.

There are three parts to this apprenticeship: a) working for current founders in the early stage of startup formation,  b) working for founders scaling teams and operations towards a profitable company, and c) finding/developing new founders-to-be.

My areas of interest are anchored by innovations in infrastructure or novel applications of such infrastructure especially mobility, network/cloud computing including hardware systems, and data intelligence. Startup teams applying novel data manipulation and machine learning to new industry verticals, building sentient hardware that is responsive to its environment and peers, and delivering software infrastructure services to developers are of high degree of interest. If you are building software, hardware, or protocols that you intend to sell to data centers, service providers, or enterprise to solve meaningful problems for these customers, I would love to figure out a way to be helpful. A particular focus of mine with early stage teams is assessing if you are taking sufficient risk or are taking the right risks as early as possible in order to solve the right problems for your users/customers.

In this period of learning, I need to revisit what I knew, fertilize it with what I learnt, and gear up for the next decade.

I serve as an apprentice in order to learn the craft of venture beyond a journeyman level – from founders and investors alike. And I welcome your suggestions and advice – @rohit_x_ or [email protected]

 

Startup culture and org charts

For startups in their first phase of building product to find the market fit, organization design is a unique challenge. Typically between two and ten employees, the organization in this phase is evolving fast and often (mostly) there isn’t a structure stable enough to be expressed in an org chart. For the founders, it is important to understand that in this early phase your org is a form to embody and express your culture. Saying “we have a flat org” is a cultural statement but may not convey much useful information to your early employees trying to find the boundaries of their work and decision making flow.

Culture in this early phase is an expression of founder DNA melded together with the DNA of early employees and undergoing evolution as a response to its environment. In fact, evolution is a very useful analogy to understand startup orgs. Startups are not products of creationism by a single supremely gifted founder or investor.  They evolve continuously influenced by the source DNA but are not limited by it if they build the right culture and communications as they add employee DNA to the mix. Added to this starter set of conditions is the DNA of all other key employees. This organism responds to market conditions, competition, technology, and is trying to find a way to survive and thrive and grow.
Culture = Founder DNA + Employee DNA + Environment + Adaptation
A critical factor in early org design is the requirement that the founder(s) understand their own skillset – in a brutally honest way and then recruit + surround themselves with people skilled in everything the founder(s) are not. Doing so also ensures that hierarchical structure and thinking does not set in early. The right hierarchy in organization is required at some point but likely not when a startup has less than ten employees. As I have written elsewhere, there are four canonical horsemen of successful startups:

The eternal optimists (usually the founders) – these folks have an uncanny ability to freeze everything except their vision of the product in a future time.
Grumpy ass kickers (usually the early employees) – bound  to create reality, they crave reality and making things vs. dreaming them up, and
Intuitive humanists (sometimes the founders, usually later employees) – they care about the human emotional needs of employees and the collective org. They will build links where none exist and are a much needed part of care and feeding of startup employees.
Chameleons (sometimes tech-founders) – these are the folks that can play whichever roles are required (dev, production, design, …) for a short duration to get-stuff-done. Finding a stable, long-term role for them in a large startup on its way to becoming a company is typically quite hard. Chameleons love startups and will hop to another one vs. scaling/changing with a single startup.

Org charts are also a proxy for communications in a startup. Silos of information appear when the charts are not heavily interconnected. An early sign of such dysfunction is the onset of process in a startup. Process != communications and when used as an excuse for not communicating usually leads to an early demise for the startup. In addition to communicating product, technology, mission, and their vision for a startup, the founders must also ensure that everyone understands the ephemeral nature of their organization and org-charts. They need to have everyone understand that:

  • The org is going to change.
  • The org is going to be flat with identified decision flows. Information must flow everywhere.
  • If you cannot look back and say what you did last week, something is not right.
  • Responsibilities assigned to everyone enables them to operate out of their comfort zone – you, the founder is likely already out of your comfort zone.
  • The org chart helps delineate responsibilities – especially for decisions.

Does your startup have an org-chart? Do you think you need one? Help me think more about this topic – @rohit_x_

 

Why Enterprise Matters

At the beginning of this century in year 2000, the average enterprise worker was using technology at least on par with anything else available elsewhere. By 2010, consumer technology had surpassed enterprise technology and products in just about every conceivable way. Google empowered every consumer to access information faster, cheaper while Facebook delivered a worldwide platform capable of handling a Billion users with few tens of milliseconds of latency, composite apps, and capable of ingesting tens of terabytes of new data every day. iPhones, Android, and iPads brought mobility to media, web, and apps while Twitter was on its way to becoming the messagebus of the world. Consumers could store and sync across devices and locations with Dropbox or Box, or stream movies on demand direct to most screens with Netflix. It is clear that 2000-2010 was the decade of consumer computing.

Looking forward to the next ten years, there is increasing excitement about the Enterprise market in silicon valley. I think we have seen waves of innovation form and deliver change in enterprise: the first big wave was the introduction of computing, the second was the Internet, and the third is a combination of cloud, big-data, and mobility. 

There are some clear reasons for this enthusiasm:

First, for about a decade and a half, technology startups have tried to sell solutions that can be broadly classified as either faster, cheaper, or more efficient. Touting ‘lower TCO’ or ‘lower opex’, these products aimed to save money for their customers but never really enabled new products or revenue for their customers. Today, for the first time since the arrival of Internet as a business reality, startups are creating products for the enterprise that enable the enterprise to create new lines of business and new revenue in addition to being faster/cheaper. This is particularly true in Enterprise Infrastructure where consumer platforms have already demonstrated scale, ability, and innovation never seen in enterprise products. Today, there is no reason why the technology stack in use at Twitter or Facebook should not be leveraged for GE or Walmart or the next 5000 enterprise IT operations – not merely to save them money, rather to enable new lines of business and revenue so that they can compete against new online businesses.

Second, software-as-a-service is now the dominant mode of delivery of most new applications for enterprise. While enterprise CIOs are changing over to this service-driven app consumption model, they are also facing a simultaneous change across all their resource layers – Computation, Storage , and Networking. Amazon and to some extent Google have proven you can leverage resources available anywhere (Cloud) across the Internet for your business operations whether you are a startup or the largest enterprise on the planet. This dual change presents most enterprises with perhaps the most complex challenge since the arrival of desktop computers for their workforce.  Solving this challenge will create large and long lasting opportunities for many startups and established vendors (IBM) alike. As if this challenge wasn’t enough, mobility is now a potential standard feature across all applications and requires deep changes to presentation, logic, and database layers. Startups targeting this particular field must remember that the “S” in SDN or SDDC must stand for “service”, not software.

And finally, the ability to manipulate, transform, and store unprecedented amounts of data (Big Data) for enterprise enables them to derive real-time intelligence and actionable information for their customers, partners, and suppliers. This Data-Intelligence segment alone has the potential to create a market as momentous as the Enterprise Software market that began with the founding of Systemanalyse und Programmentwicklung (“System Analysis and Program Development“) in 1972, Software Development Labs in 1977 and Gupta Software in 1984.

Taken together, these innovations promise to deliver productivity gains that will rival those delivered by the first two waves (computing, Internet) in the enterprise.  Such productivity boost, coupled with new business capabilities has the potential to increase revenue and boost profit margins for enterprise customers that can leverage these advances. This is the beginning of a great new enterprise market for startups.

It is time to build the future of enterprise – starting now.

 

Notes for Infrastructure Post

Notes for Infrastructure Investments blog post:

This is a list of points from my notebook over the past year collected over various meetings and conferences.

Mobility

  • Consumer mobile behaviour matters – leading indicator broadly for mobility
  • Think beyond current devices (3-5″ smartphones, 7-11″ tablets) – what devices/screens matter in 2015 ?
  • Pay attention to the rise of infrastructure apps (How mobility connects information silos)
  • Composite Apps matter more vs. individual apps (IFTTT + hardware + ambience awareness)
  • Role of data in mobile architectures (Virtual cell definitions, moving beyond ‘circuit’ connections to a single base station). Multiple radios (WiFi, 4G/LTE, …)
  • Offloading mobile traffic to data-centers vs. core-networks.
  • Mobile is not a “second screen”
  • Think “Interaction” when you think of mobile screens, not “presentation”

Cloud & Control

  • Management & Control frameworks for heterogenous hardware
  • Service-to-service information exchange with policy/compliance
  • Stupid simple ways to deliver app-aware performance (no QoS please), solve by sufficiency/availability of resources, not strict reservation.
  • Cloud-to-cloud resource signaling/advertisement/reservation
  • Software defined networking v1.0 was MPLS (remember Ipsilon), pay more attention to protocols vs. systems/boxes. Global knowledge neither required, nor assumed for optimal/practical TE.
  • Data-center to network boundary+Control matters.

Custom Hardware

  • Software defined hardware (is there any other kind?)
  • Processor controlled modules for specific workloads (across consumer/enterprise/ServiceProvider/DataCenters)
  • Software-defined Networking hardware required: backplanes, Top-of-rack switches, Data-center fabrics, DC-to-DC core networking vs. CO-to-CO (Flows/mobile-traffic/…)
  • IO bottlenecks need to be solved – scale (Users/apps) begets throughput problems.

Data Intelligence

  • New BI stack on Google/Amazon infrastructure vs. specialized warehouses
  • DI stack = presentation/visualization + Infrastructure-smarts (SW, HW) + Federated DI warehouses + DR/HA + flex-scalable db + Caching + …
  • Optimize cost per bit/byte of [store, manipulate, move, serve]
  • Infrastructure apps play a big role here, as does custom hardware (workload specific compute/store/network)
  • 2000-2010 was v0.1 (MapReduce), think Dremel, Cassovary, Spark & Shark,…

 

Infrastructure Opportunities for Startups

Over the past month I have focused on long term opportunities in Infrastructure Investment. The areas of interest to me outlined here are an invitation for founder-conversations for a venture adventure.

A good qualifier I use is “Is this a 10 year market?” – putting this simple question at the beginning of a discussion and gently raising it during founder pitches for their vision has served to separate chaff from meaningful kernels.

Another good filter is ‘enterprise‘ as the end-customer. Even if a startup is selling to intermediaries like service providers, at some point, most infrastructure “solutions” are consumed by, and hence defined-by enterprise customers.

Following this criteria, I find the following four areas highly interesting:

  • Mobility
  • Cloud & Control
  • Custom Hardware
  • Data Intelligence

Mobility

We’re now definitively entering the era of smartphones, tablets, and other screens enabling mobility for just about every experience. Historically treated via ‘gateways’ or addressed by merely reformatting pixels for the mobile screen, mobility is an aspect that is as much about context as it is about the specific features/information selected for display/interaction on a mobile screen.

In a world of touch interfaces, ‘swipe’ gestures, and easy eye-tracking for display control, the nature of information presentation and interaction changes in a fundamental way. Database, partitioning, load-balancing, caching, front-end serving choices, etc are all up for optimization once you factor in mobility. Unless addressed at every layer of the solution, mobility will break more applications than it enhances and deserves to be treated as a fundamental factor in engineering infrastructure for any service/application.

Thinking further, advances in mobile frameworks like Musubi (Mobisocial Group, Stanford) permit serverless app development by leveraging the smarts in any smartphone while honoring user privacy for all personal/social data. Efforts like these have the potential to reinvent mobility for the next decade.

Cloud & Control

I believe the ‘control’ layer for enterprise, data-center, and service provider infrastructure (private, public, hybrid) is up for grabs for the first time in infrastructure history. None of the large technology vendors: Cisco, EMC/Vmware, Citrix, HP, IBM,… have a defensible position or a compelling solution here. More than merely an OS (open) or resource-aware hypervisor, this layer needs to address policy and compliance as much as it needs to deliver service supervision, quality at the right cost and performance across sub-clouds.

As enterprises begin to consume a rapidly increasing set of software services hosted elsewhere, these services will be required to exchange information between themselves. This exchange of information also needs to occur with compliance and following customer policies. At some point, a TIBCO of cloud(s) also needs to exist. The control layer I envision needs to interact with all the constituent parts of a cloud based, cloud-delivered set of services. Nicira and other’s vision of SDNs are early pointers to the kind of protocol work yet to be done to support service-aware traffic engineering goals over data center and core networks.

In physical infrastructure, at some point, a simple and stupid-fast optical layer in the service of flexible IP networks will eventually happen without jurassic interface definitions like OTN. Physical layer innovations that lead to simplicity will scale – and win.

In the end, the cloud is less about technology – it is about providing the right business solution. Yes, innovative (and open) technology is required but in the end, technology is the easiest part of the stack.

Custom Hardware (Workload optimized Computing)

The most meaningful advances in computing, networking, and storage have come from consumer focused webscale companies in the past decade: Yahoo, Google, Amazon, Facebook, Twitter. Most of these innovations will have applications directly in the enterprise world over the next ten years.

Innovations like the low latency, throughput optimized hi1.4xlarge instance announced by AWS and benchmarked by Netflix in July 2012 are now available on demand without spending a dime on custom engineering, maintenance, or revisions. The software (application) layer needs to do precisely nothing to leverage this hardware innovation – it just runs (faster)!

This is the eventual promise of cloud – ideally it should know no bounds and permit a variety of specialized hardware to be utilized without requiring a change in *any* software. We are approaching the end of commodity hardware machines and entering the era of custom (re)configurable hardware subservient to software.

Data Intelligence (DI)

Moving beyond the first generation of large scale data processing tools (GFS, MapReduce, Hadoop, Pig, Hive,…) we’re now entering phase-2 of wringing intelligent insights from Big Data interactively, and in near real time.

BigQuery (Dremel) at Google, Cassovary at Twitter are examples of easy to use (for programmers) services that scale with ease from tens to thousands of servers and return instantaneous results across diverse data sets. The old/conventional paradigm of batch-processing required Business Analysts, the eventual users of these tools, to work with programmers/IT or learn frameworks like SQL, SAS, R,.. in order to run, modify, and redefine queries of interest. While it won’t turn mere mortal Business Analysts in to data scientists, it meaningfully reduces the ‘process’ friction and conventional-IT cooperation required in order to get the desired intelligence out of data.  Suitable only for developers today given their data models and querying languages, the interactive nature of these tools will make it attractive for startups to build data visualization, manipulation, analytics, and reporting tools on top of these magnificent data processing frameworks. The resultant Agility in business processes directly and positively enables business model innovation.

Thank you for reading this far. Some working notes for this post are here.